Using multimodal learning analytics to understand effects of block‐based and text‐based modalities on computer programming

• Published in: Journal of computer assisted learning Vol. 40; no. 3; pp. 1123 - 1136
• Main Authors: Sun, Dan, Ouyang, Fan, Li, Yan, Zhu, Chengcong, Zhou, Yang
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.06.2024; Wiley Subscription Services, Inc
• Subjects: block‐based programming; CAI; Cluster analysis; Clustering; Computer assisted instruction; Computer programming; Debugging; Education; Empirical analysis; Learning analytics; multimodal learning analytics; Programming; Secondary Education; Sequential analysis; Skills; Teaching methods; text‐based programming; Thinking Skills
• ISSN: 0266-4909; 1365-2729
• DOI: 10.1111/jcal.12939

Background With the development of computational literacy, there has been a surge in both research and practice application of text‐based and block‐based modalities within the field of computer programming education. Despite this trend, little work has actually examined how learners engaging in programming process when utilizing these two major programming modalities, especially in the context of secondary education settings. Objectives To further compare programming effects between and within text‐based and block‐based modalities, this research conducted a quasi‐experimental research in China's secondary school. Methods An online programming platform, Code4all, was developed to allow learners to program in text‐based and block‐based modalities. This research collected multimodal data sources, including programming platform data, process data, and performance data. This research further utilized multiple learning analytics approaches (i.e., clustering analysis, click stream analysis, lag‐sequential analysis and statistics) to compare learners' programming features, behavioural patterns and knowledge gains under two modalities. Results and Conclusions The results indicated that learners in text‐based modality tended to write longer lines of code, encountered more syntactical errors, and took longer to attempt debugging. In contrast, learners in block‐based modality spent more time operating blocks and attempt debugging, achieving better programming knowledge performances compared to their counterparts. Further analysis of five clusters from the two modalities revealed discrepancies in programming behavioural patterns. Implications Three major pedagogical implications were proposed based on empirical research results. Furthermore, this research contributed to the learning analytics literature by integrating process‐oriented and summative analysis to reveal learners' programming learning quality. Lay Description What is currently known about the subject matter Programming has the potential to improve learners' higher‐order thinking skills. Block‐based and text‐based modalities are two major instructional methods. There has been a growing interest to understand how learning occurs in two modes. Most previous work has evaluated two modalities based on learners' knowledge, skills, and attitudes. What this paper adds Code4all allows learners to programming in text‐based and block‐based modalities. Quasi‐experimental research was conducted to examine block‐based and text‐based programming modalities. Multimodal learning analytics were used to compare programming under two modalities. Learners' programming features, behaviouralbehavioral patterns, and knowledge gains were identified under two modalities. The implications of study findings for practitioners Instructors should integrate text‐based and block‐based modalities into programming courses. Process‐oriented assessment should be integrated with summative assessment. Adaptive, timely scaffoldings should be provided with the external support (this should be marked like above two points).